SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1671–1680 of 3073 papers

TitleStatusHype
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies—0
Oracle-guided Contrastive Clustering—0
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System—0
Outlier Guided Optimization of Abdominal Segmentation—0
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples—0
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition—0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Paladin: an annotation tool based on active and proactive learning—0
PAL : Pretext-based Active Learning—0
PANFIS++: A Generalized Approach to Evolving Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified